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feat(extraction-cache): structured telemetry fields (SKY-8870) (#5482)

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Aaron Perez 2026-04-13 19:58:33 -05:00 • committed by GitHub
parent 92b10112bd
commit 55a7966193
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5 changed files with 306 additions and 66 deletions
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58
skyvern/webeye/actions/handler.py
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@ -4288,25 +4288,59 @@ async def extract_information_for_navigation_goal(
local_datetime=local_datetime_str,
)
# Best-effort cache lookup — any failure falls through to LLM.
# Best-effort cache lookup — any failure falls through to LLM. The `try`
# is narrowed to just compute_cache_key + lookup so a downstream log
# failure can't re-enter the except block and double-count the call as
# both a hit/miss and a `lookup_error` in the Datadog miss-reason metric.
cache_key: str | None = None
lookup_result: extraction_cache.LookupResult | None = None
try:
cache_key = extraction_cache.compute_cache_key(
rendered_prompt=extract_information_prompt,
llm_key=llm_key_override,
)
cached = extraction_cache.get(task.workflow_run_id, cache_key)
if cached is not None:
LOG.info(
"extract_information cache hit — skipping LLM call",
task_id=task.task_id,
workflow_run_id=task.workflow_run_id,
cache_key=cache_key,
)
return ScrapeResult(scraped_data=cached)
lookup_result = extraction_cache.lookup(task.workflow_run_id, cache_key)
except Exception:
LOG.warning("extract_information cache lookup failed; falling through to LLM", exc_info=True)
cache_key = None
LOG.warning(
"extract_information cache lookup failed; falling through to LLM",
task_id=task.task_id,
workflow_run_id=task.workflow_run_id,
cache_key=cache_key,
cache_hit=False,
cache_scope=extraction_cache.SCOPE_RUN,
cache_age_seconds=None,
fallback_reason=extraction_cache.FALLBACK_LOOKUP_ERROR,
cache_path="agent",
exc_info=True,
)
# Preserve cache_key so the downstream store() can still warm the cache
# for subsequent identical calls even when lookup() fails transiently.
if lookup_result is not None and lookup_result.hit:
LOG.info(
"extract_information cache hit — skipping LLM call",
task_id=task.task_id,
workflow_run_id=task.workflow_run_id,
cache_key=cache_key,
cache_hit=True,
cache_scope=lookup_result.scope,
cache_age_seconds=lookup_result.age_seconds,
fallback_reason=None,
cache_path="agent",
)
return ScrapeResult(scraped_data=lookup_result.value)
if lookup_result is not None:
LOG.info(
"extract_information cache miss",
task_id=task.task_id,
workflow_run_id=task.workflow_run_id,
cache_key=cache_key,
cache_hit=False,
cache_scope=lookup_result.scope,
cache_age_seconds=None,
fallback_reason=lookup_result.fallback_reason,
cache_path="agent",
)
# Use the appropriate LLM handler based on the feature flag
llm_api_handler = LLMAPIHandlerFactory.get_override_llm_api_handler(